Recent Posts - Page 22#
Boosting Computational Fluid Dynamics Performance with AMD Instinct™ MI300X
The blog introduces CFD Ansys Fluent benchmarks and provides hands-on guide on installing and running four different Fluent models on AMD GPUs using ROCm.
Triton Inference Server with vLLM on AMD GPUs
This blog provides a how-to guide on setting up a Triton Inference Server with vLLM backend powered by AMD GPUs, showcasing robust performance with several LLMs
Training Transformers and Hybrid models on AMD Instinct MI300X Accelerators
This blog shows Zyphra's new training kernels for transformers and hybrid models on AMD Instinct MI300X accelerators, surpassing the H100s performance
Transformer based Encoder-Decoder models for image-captioning on AMD GPUs
The blog introduces image captioning and provides hands-on tutorials on three different Transformer-based encoder-decoder image captioning models: ViT-GPT2, BLIP, and Alpha- CLIP, deployed on AMD GPUs using ROCm.
SGLang: Fast Serving Framework for Large Language and Vision-Language Models on AMD Instinct GPUs
Discover SGLang, a fast serving framework designed for large language and vision-language models on AMD GPUs, supporting efficient runtime and a flexible programming interface.
Quantized 8-bit LLM training and inference using bitsandbytes on AMD GPUs
Learn how to use bitsandbytes’ 8-bit representations techniques, 8-bit optimizer and LLM.int8, to optimize your LLMs training and inference using ROCm on AMD GPUs
Introducing AMD's Next-Gen Fortran Compiler
In this post we present a brief preview of AMD's Next-Gen Fortran Compiler, our new open source Fortran complier optimized for AMD GPUs using OpenMP offloading, offering direct interface to ROCm and HIP.
Distributed Data Parallel Training on AMD GPU with ROCm
This blog demonstrates how to speed up the training of a ResNet model on the CIFAR-100 classification task using PyTorch DDP on AMD GPUs with ROCm.
CTranslate2: Efficient Inference with Transformer Models on AMD GPUs
Optimizing Transformer models with CTranslate2 for efficient inference on AMD GPUs
Torchtune on AMD GPUs How-To Guide: Fine-tuning and Scaling LLMs with Multi-GPU Power
Torchtune is a PyTorch library that enables efficient fine-tuning of LLMs. In this blog we use Torchtune to fine-tune the Llama-3.1-8B model for summarization tasks using LoRA and showcasing scalable training across multiple GPUs.
Inference with Llama 3.2 Vision LLMs on AMD GPUs Using ROCm
Meta's Llama 3.2 Vision models bring multimodal capabilities for vision-text tasks. This blog explores leveraging them on AMD GPUs with ROCm for efficient AI workflows.
Speed Up Text Generation with Speculative Sampling on AMD GPUs
This blog will introduce you to assisted text generation using Speculative Sampling. We briefly explain the principles underlying Speculative Sampling and demonstrate its implementation on AMD GPUs using ROCm.